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# Machine Learning Engineer, Speech LLM Training - San Francisco

- **Company:** PLAUD INC.
- **Location:** San Francisco, CA, United States
- **Salary:** $195,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Clusters, Software Code Optimization, Codecs, Cyber Security, Data Security, Software Debugging, Machine Learning, Performance Tuning, Signal Processing, Privacy Controls, Reinforcement Learning, Pytorch, DeepSpeed, Large Language Models, Generative AI, Kubernetes, SGLang, TensorRT
- **Published:** October 22, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-engineer-speech-llm-training-san-francisco-san-francisco-ca--1e450af9-38c2-418a-b5d8-6d573f56081a

## About the Role

Machine Learning, Artificial Intelligence (AI), Power Amplifier, Human Intelligence (HUMINT), HIPAA (Health Insurance Portability and Accountability Act), ISO (International Organization for Standardization), Privacy Controls, Information/Data Security (InfoSec), Pro Tools, Career Development, * Have a proven track record of building and training large-scale audio or speech models from the ground up, whether that involves unified SpeechLLMs, advanced ASR, expressive TTS, or generative audio architectures.

 * Love living at the intersection of research and engineering, eager to design novel sequence modeling architectures one day and debug distributed training clusters the next.
 * Are highly comfortable traversing the entire stack-from fundamental signal processing and raw acoustic representations to massive foundation model training and edge-device optimization.
 * Possess deep expertise in PyTorch or JAX, with battle scars from optimizing large-scale distributed training runs, managing GPU memory utilization, and resolving complex performance bottlenecks.
 * Thrive in a fast-paced, high-growth startup environment where you are expected to take extreme ownership of ambiguous problems and drive them directly into production.
 * Are obsessed with building AI systems that natively understand and generate speech, ultimately creating a hardware-software AI companion that amplifies human productivity.

Strong candidates may also have experience with:

 * Text-based LLMs: Hands-on experience with core text-based Large Language Model pretraining, instruction tuning, or RLHF.
 * Neural Audio Codecs: Hands-on experience designing and training state-of-the-art neural audio codecs for streamable, high-fidelity audio.
 * Generative Architectures: Designing and training diffusion models, flow matching, or autoregressive architectures specifically for speech and voice generation.
 * Alignment & Steerability: Applying Reinforcement Learning (RL) techniques (like RLHF or GRPO) to improve conversational cadence, steerability, and alignment in foundation models.
 * Deep System Optimization: End-to-end inference and performance optimization, leveraging high-throughput serving frameworks (e.g., vLLM, TensorRT-LLM, SGLang) to minimize latency for real-time cloud streaming.
 * Large-Scale Infrastructure: Managing massive GPU clusters, utilizing advanced distributed training frameworks (e.g., FSDP, DeepSpeed), and navigating orchestration tools like Kubernetes.